A Measure of Explanatory Effectiveness

The term explanation in artificial intelligence (AI) is often conflated with the concepts of interpretability and explainable AI (XAI), but there are important distinctions to be made. Miller (2019) defines interpretability and XAI as the process of building AI systems that humans can understand. In other words, by design, the AI's decision-making process is inherently transparent to a human. In contrast, explicitly explaining the decision-making to an arbitrary human is explanation generation. The latter is the subject of this paper. More specifically, we are working towards developing a formal framework for the automated generation and assessment of explanations. Firstly, some key terminology: an explanation is generated through a dialectical interaction whereby one agent, the explainer, seeks to'explain' some phenomenon, called the explanandum, to another agent, the explainee. In this article, we propose a novel measure of explanatory effectiveness that can be used to motivate artificial agents to generate good explanations (e.g. in the form of a reward signal), or to analyse the behaviours of existing communicating agents. We then define explanation games as cooperative games where two (or more) agents seek to maximise the effectiveness measure.